In this proposal, we present a variety of methods that will significantly lower identification time of any face recognition algorithm by reducing the overall number of “possible subjects” through the use of Indexing and Soft Biometrics. Based on our prior academically published experience in this area and current Phase II SBIR research and development (ONR N08-077 – Automated Entity Classification in Video Using Soft Biometrics), we will construct a solution that leverages soft biometric features (i.e. gender, age, ethnicity, hair color, etc.) in order to categorize individuals into specific predefined bins. These features can be extracted during the preprocessing or enrollment phases and used to develop a novel Feature Search Tree (FST) data structure to enable significantly improved facial recognition. An added benefit of our soft biometric approach is that learned features can be combined using our innovative “Morphable Template Model” in order to automatically construct galleries of high resolution photo-realistic facial images of subjects matching textual descriptions.